Analyzing and forecasting the Chinese term structure of interest rates using functional principal component analysis

Purpose - The purpose of this paper is to analyze and forecast the Chinese term structure of interest rates using functional principal component analysis (FPCA). Design/methodology/approach - The authors propose an FPCA-K model using FPCA. The forecasting of the yield curve is based on modeling functional principal component (FPC) scores as standard scalar time series models. The authors evaluate the out-of-sample forecast performance using the root mean square and mean absolute errors. Findings - Monthly yield data from January 2002 to December 2016 are used in this paper. The authors find that in the full sample, the first two FPCs account for 98.68 percent of the total variation in the yield curve. The authors then construct an FPCA-K model using the leading principal components. The authors find that the FPCA-K model compares favorably with the functional signal plus noise model, the dynamic Nelson-Siegel models and the random walk model in the out-of-sample forecasting. Practical implications - The authors propose a functional approach to analyzing and forecasting the yield curve, which effectively utilizes the smoothness assumption and conveniently addresses the missing-data issue. Originality/value - To the best knowledge, the authors are the first to use FPCA in the modeling and forecasting of yield curves.